I think you should switch Type 1 and Type 2 error in the table (at time 3"30). Reference: stanford.edu/~shervine/teaching/cs-229/cheatsheet-machine-learning-tips-and-tricks
Hi Elizabeth, I think you have your numbers wrong in this video. If the mean = 500 and the SD = 100, then 533 is a z-score = 0.33, where z = (value - sample mean) ÷ SD = (533-500)÷100 = 0.33 Also a z-score = 1.645 (which is p = .05) would be a value of 664.5, where new value = (z-score x SD) + sample mean = (1.645 x 100) + 500 = 664.5 Or am I totally missing something here? Other than that, I watched all three of the related videos and they helped me grasp--again--what power is. Oh...I think the category you have set for the videos is "comedy". You might want to change that.
I realize this is an old comment, but for anyone else with the same question. She's using the standard error which would divide the standard deviation by the square root of the sample size. The standard error is the standard deviation of a sampling distribution.
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very nice explanation
I think you should switch Type 1 and Type 2 error in the table (at time 3"30). Reference: stanford.edu/~shervine/teaching/cs-229/cheatsheet-machine-learning-tips-and-tricks
This is a great video. Very clear and concise. Thank you so much for posting it!
Thank you. Great video
This was great! Super clear.
Hi Elizabeth, I think you have your numbers wrong in this video. If the mean = 500 and the SD = 100, then 533 is a z-score = 0.33, where z = (value - sample mean) ÷ SD = (533-500)÷100 = 0.33
Also a z-score = 1.645 (which is p = .05) would be a value of 664.5, where new value = (z-score x SD) + sample mean = (1.645 x 100) + 500 = 664.5
Or am I totally missing something here? Other than that, I watched all three of the related videos and they helped me grasp--again--what power is.
Oh...I think the category you have set for the videos is "comedy". You might want to change that.
I realize this is an old comment, but for anyone else with the same question.
She's using the standard error which would divide the standard deviation by the square root of the sample size. The standard error is the standard deviation of a sampling distribution.
this video helped me alot…thank you